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Generalized Linear Models: A Bayesian Perspective
Abstract
This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers
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Citations
Cited by
Rue, H., Martino, S., & Chopin, N. (2009) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00700.x · Cited by 5,279
Bayesian hypothesis testing for psychologists: A tutorial on the Savage–Dickey method
Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R. (2010) · Cognitive Psychology · doi:10.1016/j.cogpsych.2009.12.001 · Cited by 740
Faraway, J. J. (2016) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315382722 · Cited by 584
Bayesian auxiliary variable models for binary and multinomial regression
Held, L., & Holmes, C. C. (2006) · Bayesian Analysis · doi:10.1214/06-ba105 · Cited by 434
Generalized structured additive regression based on Bayesian P-splines
Brezger, A., & Lang, S. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.10.011 · Cited by 371
Metadata
- Venue
- CRC Press eBooks
- Publisher
- CRC Press
- Published
- 2000-05-25
- OpenAlex
- W1584127072
- Cited by
- 266
- References
- 0
- Type
- reference entry
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- CoChat. (2026). Citation check: Generalized Linear Models: A Bayesian Perspective. https://app.cochat.ai/papers/10.1201/9781482293456
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- Generalized Linear Models: A Bayesian Perspective. (2000). CRC Press eBooks. https://doi.org/10.1201/9781482293456
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
